Quantitative Remote Sensing of Land Surfaces

By Shunlin Liang

Processing the vast amounts of data on the Earth's land surface environment generated by NASA's and other international satellite programs is a significant challenge. Filling a gap between the theoretical, physically-based modelling and specific applications, this in-depth study presents practical quantitative algorithms for estimating various land surface variables from remotely sensed observations.
A concise review of the basic principles of optical remote sensing as well as practical algorithms for estimating land surface variables quantitatively from remotely sensed observations.
Emphasizes both the basic principles of optical remote sensing and practical algorithms for estimating land surface variables quantitatively from remotely sensed observations
Presents the current physical understanding of remote sensing as a system with a focus on radiative transfer modelling of the atmosphere, canopy, soil and snow
Gathers the state of the art quantitative algorithms for sensor calibration, atmospheric and topographic correction, estimation of a variety of biophysical and geoph ysical variables, and four-dimensional data assimilation

• Language: English
• Publisher: Wiley-Interscience
• Release date: Mar 11, 2005
• ISBN: 9780471723714

Shunlin Liang

Shunlin Liang is Chair Professor at the Department of Geography, University of Hong Kong. His main research interests focus on estimating land surface essential variables from satellite data, global satellite product generation, and global environmental change. He is a Fellow of the IEEE and an Editor-in-Chief of Science of Remote Sensing. He has published over 430 peer-reviewed journal papers, 10 special issues of different journals, and authored/edited eight books. He was also Editor-in-Chief of the nine-volume reference work “Comprehensive Remote Sensing” (Elsevier, 2017) and has led to develop the Global LAnd Surface Satellite (GLASS) products suite and the Hi-GLASS products.

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